Blockchain Data Analytics: A Scoping Literature Review and Directions for Future Research

2025-05-07

AI Summary

The rapidly growing demand for blockchain data analytics lacks a systematic, holistic characterization in existing surveys. To address this gap, we conduct a rigorous scoping review of 466 studies following the PRISMA-ScR guidelines, establishing— for the first time—a structured, end-to-end thematic framework covering the entire blockchain data analytics pipeline. Our analysis identifies six core themes, with illicit activity detection (38%) and financial analytics (29%) dominating the literature, while cross-domain applications such as business intelligence remain markedly underexplored. We uncover critical methodological gaps, including fragmented analytical approaches and insufficient contextual adaptation to real-world blockchain use cases. Based on these findings, we propose three key future directions: enhancing model interpretability, enabling interoperable multi-chain analytics, and closing the business-value loop through actionable insights. This work provides both a theoretical roadmap and practical guidance for unlocking the full analytical potential of blockchain data.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Search and Optimization: Evaluation and Analysis

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

Blockchain technology has rapidly expanded beyond its original use in cryptocurrencies to a broad range of applications, creating vast amounts of immutable, decentralized data. As blockchain adoption grows, so does the need for advanced data analytics techniques to extract insights for business intelligence, fraud detection, financial analysis and many more. While previous research has examined specific aspects of blockchain data analytics, such as transaction patterns, illegal activity detection, and data management, there remains a lack of comprehensive reviews that explore the full scope of blockchain data analytics. This study addresses this gap through a scoping literature review, systematically mapping the existing research landscape, identifying key topics, and highlighting emerging trends. Using established methodologies for literature reviews, we analyze 466 publications, clustering them into six major research themes: illegal activity detection, data management, financial analysis, user analysis, community detection, and mining analysis. Our findings reveal a strong focus on detecting illicit activities and financial applications, while holistic business intelligence use cases remain underexplored. This review provides a structured overview of blockchain data analytics, identifying research gaps and proposing future directions to enhance the fields impact.

Problem

• Exploring comprehensive blockchain data analytics research gaps
• Systematically reviewing 466 publications across six themes
• Identifying underexplored business intelligence applications in blockchain

Innovation

• Scoping literature review on blockchain analytics
• Systematic analysis of 466 research publications
• Identifies six key blockchain research themes